ScratchMaths: evaluation report and executive summary

Since 2014, computing has been part of the primary curriculum. ‘Scratch’ is frequently used by schools, and the EEF funded this trial to test whether the platform could be used to improve pupils’ computational thinking skills, and whether this in turn could have a positive impact on Key Stage 2 maths attainment. Good computational thinking skills mean pupils can use problem solving methods that involve expressing problems and their solutions in ways that a computer could execute – for example, recognising patterns. Previous research has shown that pupils with better computational thinking skills do better in maths. The study found a positive impact on computational thinking skills at the end of Year 5 – particularly for pupils who have ever been eligible for free school meals. However, there was no evidence of an impact on Key Stage 2 maths attainment when pupils were tested at the end of Year 6. Many of the schools in the trial did not fully implement ScratchMaths, particularly in Year 6, where teachers expressed concerns about the pressure of Key Stage 2 SATs. But there was no evidence that schools which did implement the programme had better maths results. Schools may be interested in ScratchMaths as an affordable way to cover aspects of the primary computing curriculum in maths lessons without any adverse effect on core maths outcomes. This trial, however, did not provide evidence that ScratchMaths is an effective way to improve maths outcomes

Study protocol: SWING – social capital and well-being in neighborhoods in Ghent

Background: Investing in social capital has been put forth as a potential lever for policy action to tackle health inequity. Notwithstanding, empirical evidence that supports social capital's role in the existence of health inequity is limited and inconclusive. Furthermore, social capital literature experiences important challenges with regard to (1) the level on which social capital is measured and analyzed; (2) the measurement of the concept in line with its multidimensional nature; and (3) the cross-cultural validity of social capital measurements. The Social capital and Well-being In Neighborhoods in Ghent (SWING) study is designed to meet these challenges. The collected data can be used to investigate the distribution of health problems and the association between social capital, health and well-being, both at the individual and at the neighborhood level. The main goals of the SWING study are (1) to develop a coherent multilevel dataset of indicators on individual and neighborhood social capital and well-being that contains independent indicators of neighborhood social capital at a low level of aggregation and (2) to measure social capital as a multidimensional concept. The current article describes the background and design of the SWING study. Methods/Design: The SWING study started in 2011 and data were collected in three cross-sectional waves: the first in 2011, the second in 2012, and the third in 2013. Data collection took place in 142 neighborhoods (census tract level) in the city of Ghent (Flanders, Belgium). Multiple methods of data collection were used within each wave, including: (1) a standardized questionnaire, largely administered face-to-face interviews for neighborhood inhabitants (N = 2,730); (2) face-to-face interviews with key informants using a standardized questionnaire (N = 2,531); and (3) an observation checklist completed by the interviewers (N = 2,730 in total). The gathered data are complemented by data available within administrative data services. Discussion: The opportunities and ambitions of the SWING study are discussed, together with the limitations of the database

Multilevel modelling of refusal and noncontact nonresponse in household surveys: evidence from six UK government surveys

This paper analyses household unit nonresponse and interviewer effects in six major UK government surveys using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of survey dependent and independent effects of household and interviewer characteristics, providing an empirical exploration of the leverage-salience theory. The analysis is based on the 2001 UK Census Link Study, a unique data source containing an unusually rich set of auxiliary variables, linking the response outcome of six surveys to census data, interviewer observation data and interviewer information, available for respondents and nonrespondents

Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys

We analyse household unit non-response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non-respondents

Variation in compulsory psychiatric inpatient admission in England:a cross-sectional, multilevel analysis

Background: Rates of compulsory admission have increased in England in recent decades, and this trend is accelerating. Studying variation in rates between people and places can help identify modifiable causes. Objectives: To quantify and model variances in the rate of compulsory admission in England at different spatial levels and to assess the extent to which this was explained by characteristics of people and places. Design: Cross-sectional analysis using multilevel statistical modelling. Setting: England, including 98% of Census lower layer super output areas (LSOAs), 95% of primary care trusts (PCTs), 93% of general practices and all 69 NHS providers of specialist mental health services. Participants: 1,287,730 patients. Main outcome measure: The study outcome was compulsory admission, defined as time spent in an inpatient mental illness bed subject to the Mental Health Act (2007) in 2010/11. We excluded patients detained under sections applying to emergency assessment only (including those in places of safety), guardianship or supervision of community treatment. The control group comprised all other users of specialist mental health services during the same period. Data sources: The Mental Health Minimum Data Set (MHMDS). Data on explanatory variables, characterising each of the spatial levels in the data set, were obtained from a wide range of sources, and were linked using MHMDS identifiers. Results: A total of 3.5% of patients had at least one compulsory admission in 2010/11. Of (unexplained) variance in the null model, 84.5% occurred between individuals. Statistically significant variance occurred between LSOAs [6.7%, 95% confidence interval (CI) 6.2% to 7.2%] and provider trusts (6.9%, 95% CI 4.3% to 9.5%). Variances at these higher levels remained statistically significant even after adjusting for a large number of explanatory variables, which together explained only 10.2% of variance in the study outcome. The number of provider trusts whose observed rate of compulsory admission differed from the model average to a statistically significant extent fell from 45 in the null model to 20 in the fully adjusted model. We found statistically significant associations between compulsory admission and age, gender, ethnicity, local area deprivation and ethnic density. There was a small but statistically significant association between (higher) bed occupancy and compulsory admission, but this was subsequently confounded by other covariates. Adjusting for PCT investment in mental health services did not improve model fit in the fully adjusted models. Conclusions: This was the largest study of compulsory admissions in England. While 85% of the variance in this outcome occurred between individuals, statistically significant variance (around 7% each) occurred between places (LSOAs) and provider trusts. This higher-level variance in compulsory admission remained largely unchanged even after adjusting for a large number of explanatory variables. We were constrained by data available to us, and therefore our results must be interpreted with caution. We were also unable to consider many hypotheses suggested by the service users, carers and professionals who we consulted. There is an imperative to develop and evaluate interventions to reduce compulsory admission rates. This requires further research to extend our understanding of the reasons why these rates remain so high. Funding: The National Institute for Health Research Health Services and Delivery Research programme

A pattern-based development of secure business processes

Iga andmeturbest huvitatud äriettevõte valib iseendale sobilikud turvameetmed, et vältida ootamatuid sündmusi ja õnnetusi. Nende turvameetmete esmane ülesanne on kaitsta selle äriettevõtte ressursse ja varasid. Äriettevõtetes aset leidvad õnnetused (vähemtähtsad või katastroofilised) on enamikel juhtudel oma olemuselt sarnased ning põhjustatud sarnaste turvariskide poolt. Paljudel andmeturbe spetsialistidel on raskusi leidmaks õiget lahendust konkreetsetele probleemidele, kuna eelmiste samalaadsete probleemide lahendused ei ole korrektselt dokumenteeritud. Selles kontekstis on turvalisuse mustrid (Security Patterns) kasulikud, kuna nad esitavad tõestatud lahendusi spetsiifiliste probleemide jaoks. Käesolevas väitekirjas arendasime välja kümme turvariskidele suunatud mustrit (SRP ehk Security Risk-oriented Patterns) ja defineerisime, kuidas kasutada neid mustreid vastumeetmetena turvariskidele äriprotsesside mudelite sees. Oma olemuselt on need mustrid sõltumatud modelleerimiskeelest. Lihtsustamaks nende rakendamist, on mudelid esitatud graafilises vormingus äriprotsesside modelleerimise keeles (BPMN). Me demonstreerime turvariskidele suunatud mustrite (SRP) kasutatavust kahe tööstusettevõtte ärimudeli näite põhjal. Esitame mustrite rakendamise kohta kvantitatiivsed analüüsid ja näitame, kuidas turvariskidele suunatud mustrid (SRP) aitavad demonstreerida andmeturbe nõrku kohti ärimudelites ning pakume välja lahendusi andmeturvalisusega seotud probleemidele. Selle uurimistöö tulemused võivad julgustada andmeturvalisusega tegelevaid analüütikuid jälgima mustritel-põhinevaid lähenemisi oma äriettevõtete kaitsmiseks, et aidata seeläbi kaasa ka infosüsteemide (Information Systems (IS)) kaitsmisele.Every security concerned enterprise selects its own security measures in order to avoid unexpected events and accidents. The main objective of these security measures is to protect the enterprise’s own resources and assets from damage. Most of the time, the accidents or disasters take place in enterprise are similar in nature, and are caused by similar kind of vulnerabilities. However, many security analysts find it difficult to select the right security measure for a particular problem because the previous proven solutions are not properly documented. In this context Security Patterns could be helpful since they present the proven solutions that potentially could be reused in the similar situations. In this thesis, we develop a set of ten Security Risk-oriented Patterns (SRP) and define the way how they could be used to define security countermeasures within the business process models. In principle, patterns are modelling language-independent. Moreover, to ease their application, we represent them in a graphical form using the Business Process Modelling Notation (BPMN) modelling approach. We demonstrate the usability of the Security Risk-oriented Patterns (SRP) by applying them on two industrial business models. We present the quantitative analysis of their application. We show that Security Risk-oriented Patterns (SRP) help to determine security risks in business models and suggest rationale for security solutions. The results of this research could potentially encourage the security analysts to follow pattern-based approach to develop secure business processes, thus, contributing to secure Information Systems (IS)

Work-Unit Absenteeism: Effects of Satisfaction, Commitment, Labor Market Conditions, and Time

Prior research is limited in explaining absenteeism at the unit level and over time. We developed and tested a model of unit-level absenteeism using five waves of data collected over six years from 115 work units in a large state agency. Unit-level job satisfaction, organizational commitment, and local unemployment were modeled as time-varying predictors of absenteeism. Shared satisfaction and commitment interacted in predicting absenteeism but were not related to the rate of change in absenteeism over time. Unit-level satisfaction and commitment were more strongly related to absenteeism when units were located in areas with plentiful job alternatives

A lightweight blockchain based framework for underwater ioT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Innovative Work Behaviour: Measurement and Validation

Although both scientists and practitioners emphasize the importance of innovative work behavior (IWB) of individual employees for organizational success, the measurement of employees' IWB is still in evolution. Here, we present two multi-source studies that aimed to develop and validate a measure of IWB. Four related dimensions of IWB are distinguished: opportunity exploration, idea generation, championing and application. We derived a tenitem measure of these IWB dimensions from a pilot survey among matched dyads of 81 professionals in a research institute and their supervisors. Next, a survey among a matching sample of 703 knowledge workers and their supervisors from 94 different firms was done. We used confirmatory factor analyses to examine convergent and discriminant validity, and hierarchical multilevel regression to test hypothesized relationships of IWB with participative leadership, external work contacts and innovative output (proposed as an initial nomological network). Results demonstrate strong convergent validity of the IWB measure as all four dimensions contribute to an overall measure of IWB. Support for discriminant validity is weaker as correlations between some dimensions are relatively high. Finally, IWB is positively related with participative leadership, external work contacts and innovative output, providing first evidence for nomological validity.